这是一个判断海洋生物数据是否是鱼类而构建的基于ID3思想的决策树,供大家参考,具体内容如下
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# coding=utf-8
import operator
from math import log
import time
def createDataSet():
dataSet = [[ 1 , 1 , 'yes' ],
[ 1 , 1 , 'yes' ],
[ 1 , 0 , 'no' ],
[ 0 , 1 , 'no' ],
[ 0 , 1 , 'no' ],
[ 0 , 0 , 'maybe' ]]
labels = [ 'no surfaceing' , 'flippers' ]
return dataSet, labels
# 计算香农熵
def calcShannonEnt(dataSet):
numEntries = len (dataSet)
labelCounts = {}
for feaVec in dataSet:
currentLabel = feaVec[ - 1 ]
if currentLabel not in labelCounts:
labelCounts[currentLabel] = 0
labelCounts[currentLabel] + = 1
shannonEnt = 0.0
for key in labelCounts:
prob = float (labelCounts[key]) / numEntries
shannonEnt - = prob * log(prob, 2 )
return shannonEnt
def splitDataSet(dataSet, axis, value):
retDataSet = []
for featVec in dataSet:
if featVec[axis] = = value:
reducedFeatVec = featVec[:axis]
reducedFeatVec.extend(featVec[axis + 1 :])
retDataSet.append(reducedFeatVec)
return retDataSet
def chooseBestFeatureToSplit(dataSet):
numFeatures = len (dataSet[ 0 ]) - 1 # 因为数据集的最后一项是标签
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0
bestFeature = - 1
for i in range (numFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set (featList)
newEntropy = 0.0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet, i, value)
prob = len (subDataSet) / float ( len (dataSet))
newEntropy + = prob * calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntropy
if infoGain > bestInfoGain:
bestInfoGain = infoGain
bestFeature = i
return bestFeature
# 因为我们递归构建决策树是根据属性的消耗进行计算的,所以可能会存在最后属性用完了,但是分类
# 还是没有算完,这时候就会采用多数表决的方式计算节点分类
def majorityCnt(classList):
classCount = {}
for vote in classList:
if vote not in classCount.keys():
classCount[vote] = 0
classCount[vote] + = 1
return max (classCount)
def createTree(dataSet, labels):
classList = [example[ - 1 ] for example in dataSet]
if classList.count(classList[ 0 ]) = = len (classList): # 类别相同则停止划分
return classList[ 0 ]
if len (dataSet[ 0 ]) = = 1 : # 所有特征已经用完
return majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataSet)
bestFeatLabel = labels[bestFeat]
myTree = {bestFeatLabel: {}}
del (labels[bestFeat])
featValues = [example[bestFeat] for example in dataSet]
uniqueVals = set (featValues)
for value in uniqueVals:
subLabels = labels[:] # 为了不改变原始列表的内容复制了一下
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet,
bestFeat, value), subLabels)
return myTree
def main():
data, label = createDataSet()
t1 = time.clock()
myTree = createTree(data, label)
t2 = time.clock()
print myTree
print 'execute for ' , t2 - t1
if __name__ = = '__main__' :
main()
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最后我们测试一下这个脚本即可,如果想把这个生成的决策树用图像画出来,也只是在需要在脚本里面定义一个plottree的函数即可。
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。
原文链接:http://blog.csdn.net/gentelyang/article/details/75195630